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作 者:林萌菲[1] 张凤华[2] 潘嘉欣[2] Lin Mengfei;Zhang Fenghua;Pan Jiaxin(School of Economics and Management,Zhuhai City Polytechnic,Zhuhai 519090,China;School of Management,Guangdong University of Technology,Guangzhou 510520,China)
机构地区:[1]珠海城市职业技术学院经济管理学院,广东珠海519090 [2]广东工业大学管理学院,广东广州510520
出 处:《科技管理研究》2022年第10期123-130,共8页Science and Technology Management Research
基 金:教育部人文社会科学研究青年基金项目“顾客共创的双刃剑影响效应:基于自我提升感知的中介”(19YJC630097);广东省普通高校重点科研平台和科研项目“顾客共创的双刃剑影响效应”(2018GWTSCX085);国家自然科学基金项目“创新顾客知识融合过程的激励机制研究:以公平偏好理论为视角”(72002044)。
摘 要:针对现有研究缺乏结合创新贡献率和关系强度对创新顾客推荐激励模型进行系统探索,基于创新顾客参与新产品开发活动的重要性及其与普通顾客的差异性,构建创新顾客参与新产品开发活动的产品推荐激励模型,确定企业需要设置推荐激励计划时的条件,并以JS公司为例,通过计算机模拟仿真对模型的推导结论进行验证。结果发现,企业给予创新顾客的推荐激励强度比给予普通顾客的低,但创新顾客带来的净利润比普通顾客高;当创新顾客只向与其为强关系的朋友推荐产品/服务,或与其为强关系朋友的占比较大时,企业不需要设置推荐激励计划;随着创新顾客的创新贡献率增大,企业应减小推荐激励强度,而随着创新顾客与被推荐者之间弱关系强度增大,企业应增加推荐激励强度。研究为企业提出管理启示:应积极引导创新顾客进行产品推荐,并结合创新顾客与被推荐者的关系强度优化推荐激励计划,同时应重视创新顾客进行产品推荐的真实性。In view of the existing research lack of exploration of contribution rate and relationship strength on innovative customers’ recommendation incentive model, based on the importance of innovative customers’ participation in new product development activities and the difference with ordinary customers, this paper builds the incentive model of innovative customers’ participation in new product development activities, and determines the conditions under which enterprises need to set up the recommendation incentive plan, and takes the JS company as an example,verifies the derivation of the model by computer simulation. The research results show that the recommendation incentive intensity of innovative customers is lower than that of ordinary customers, but the net profit of innovative customers is higher than that of ordinary customers, when innovative customers only recommend products/services to friends with strong relationship strength, or the number of friends with strong relationship strength is relatively large,enterprises do not need to set up the recommendation incentive plan;with the increase of innovation contribution rate of innovative customers, enterprises should reduce the recommendation incentive intensity, and with the increase of the weak relationship strength between innovative customers and recipients, enterprises should increase the recommendation incentive intensity. The research provides management enlightenments for enterprises that enterprises should actively guide innovative customers to recommend products, optimize recommendation incentive plan based on the relationship strength between innovative customers and recipients, and attach importance to the authenticity of product recommendation by innovative customers.
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